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Bewertung und Feedback des Lernenden für Maschinelles Lernen mit Python von IBM Skills Network

4.7
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12,859 Bewertungen

Über den Kurs

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course....

Top-Bewertungen

RC

6. Feb. 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

FO

8. Okt. 2020

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.

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1601 - 1625 von 2,238 Bewertungen für Maschinelles Lernen mit Python

von Andrew B

1. Juli 2019

The rubric for the last assignment was too arbitrary. People with little to no machine learning experience will assume that submissions have to be cookie-cutter copies of previous labs in order to achieve 100%. I would put force students to put random seed on models in order to achieve similar results to achieve more homogeneity and therefore an easier way to grade. Perhaps you could put a section at the end that allows for further parameter tuning if the student so desires.

von beyda

5. Juli 2022

When I started the course, I was expecting more of python libraries and exercises with Machine Learning in python. However the lab parts was ungraded practices so there wasn't an instructor for me to tell and teach how I can imply my vision and knowledge of machine learning on python. Other than that, It's a great experience and the lessons are really clear, simple and teaching. I'm always enjoying the videos while I'm practicing and learning.

von Richard W

22. Dez. 2021

Good grounding into machine learning techniques with python. Bit slow at times and would like to have more emphasis on the application of techniques on real data sets e.g. dataset requirements and effectiveness of algorithms on datasets of varying size, and how to avoid overfitting etc. Also it appears as though the requirement to sign up to IBM Watson Studio is not actually required although you are heavily led that way.

von Martha C

16. Apr. 2021

The course is well done and covers many of the basic ML concepts. The reason I gave it 4 instead of 5 stars is because the final assignment asks you to do something that wasn't covered in the course, and it's not very clear either what they're asking you to do. I was able to figure it out, but it was a bit frustrating at the time (especially since I got all the way to the end and realized I had to do something different).

von Рыков А Г

5. Apr. 2020

This course is great for begginers. Basic theory of simpliest algorithms and techniques is given in really simple way. I enjoyed to listen to videos. However, there is not enough practice coding. Final project was the only challenging task during the course. Another drawback - misprints. In addition, goals of the final project were not clear as for me. To sum up, this course is good just for basic theory review.

von Francisco M

5. Apr. 2020

The course is good but sometimes the exercise texts are not very clear and some of the lessons are very straightforward, leaving many doubts. The course should have a larger series of exercises and an automatic correction system that facilitates the review of the exercises. In addition, it would be interesting to have a module on how to use IBMDB2 without the online platform, but through Jupyter on the computer.

von Pratik P

21. März 2022

Learning this course, and specially after Week6, I strongly felt to have some background on statistics. I had to replay vedios multiple time to get the concept. Also the content of the course is very solid, but sometime I felt there is not much explanation of approach, sometime intermediate steps reasoning is missing. Final assignment is very well laid out touching all the models you learn in this course.

von Jianxu S

13. Sep. 2019

The material is comprehensive covering almost all of the popular models. Unfortunately, the peer-graded assignment only covers classification models so the practice on clustering is lacking. For real world problems, this module is probably the most useful so it would be beneficial to include more practice on clustering for examples. Overall, it is an interesting course with lots of new ideas for beginners.

von Dorothea M

29. März 2020

I particularly enjoyed this course. It is easy to understand it even with a basic knowledge of Python. Lab exercises are well-writen and very helpful for the completion of the course. I think it's a great introduction to programming using SciKit Learn. Personally, I would have liked to learn a bit more about the mathematical background of the algorithms but maybe this is out of the scope of the course.

von Eugene B

12. Nov. 2019

Pretty good course, but you REALLY need to put in your own time to get anything out of it. You really could probably complete this course by just copy-pasting into the assignments. I wish there was slightly less hand-holding throughout the course and more having to do more work on your own with proper guidance, rather than just "here's a video" then "here's a notebook. Run it and see what happens."

von Hariharan S

26. Jan. 2022

This course is the perfect for one who learns the basic of machine learning and They will make sure you learn it percectly but i give it only 4 stars because the lab session was not explained by the instructor although it was liitle bit self explained by the notebook itself it would be better for us if you explain some tougher lines atleast.Overall the course was an excellent one.

von Amanda A

24. Apr. 2020

I enjoyed this course and felt like I learned a lot! The reason why I'm not giving 5 stars is because some of the assessments need work -- instructions and wording on questions were either confusing or contradictory (for example, on the final project you are asked to find the best k value for 4 different types of ML algorithms even though only one of them has "k value" defined).

von Islam A

26. Apr. 2020

The course was good, generally. Instructors as well. I had used IBM Watson and Jupiter Notebooks which was really usefull. But it would be great if you add more real world examples for algorithms use cases. Errors in the presentations and in the Jupyter workbooks, which were mentioned years before, and still have not been fixed are really unprofessional. Anyway, thank you.

von Stephane B

13. Jan. 2020

This course is relatively good. If you are looking for a introduction to machine learning this is the course for you as it covers most of the methods over a short period of time. The downfall of this is that the algorithms are not covers in detain in particular their optimization and limitations.

Also the exercise are done on the IBM development platform which is garbage.

von Kyle R

4. Apr. 2020

The material was good but the servers for the ungraded projects could use some work. I had connectivity issues with each project I tried to attempt and even now when I tried to reference the material to improve my models I could not access them. Other than that I thought that this course was very informative and helped me become an overall better programmer.

von Dennis K

16. Okt. 2021

It was good, but I wish the "ungraded-labs" would've been graded labs and would've forced me to do some work. I did learn a lot from the content of the videos, but having to code out each week before the final project would've helped to solidify my learning. Still a good course, and the final project does ensure that you understand what you're doing.

von Joshua S

16. Aug. 2021

Interesting course with information pertaining to the real world with clear examples to support the information. Actually, one of the few courses where the labs were useful in the real world and the final project wasn't extremely difficult. The videos were a lot to take in at one time but the material was presented in an informative way.

von Tony s

30. Mai 2020

This course is best under to understand the theory part of machine learning and this will give ou understanding about the python library ScikitLearn , logistic regression and machine leaarning wth python . But there is some missing i found while study this course is programming (coding) part which is not given by teacher.

Thanks !

von Daniel D

26. Mai 2020

This is my favorite course in The Data Science Professional Certificate. Using real-world examples we implemented several ML models using scikit learn and python. There is also some exposure to numpy. This is a good course and overall provides applied data science methods with a comparison of common methods for classification.

von Collin C

15. Jan. 2020

Valuable material and well organized. There are many gaps in the explanations though. In the sample notebooks, there is a LOT of code that is not explained, so I have to Google the code or skip over it. The final tests a skill (transferring a machine learning model to an separate database) which was never taught or addressed.

von Voranipit C

9. Juli 2021

This course is great for concept of ML good enough for applying but not the best for who try to understand under the hood of ML

It's can go future if you need to know more math behind ML you need to take another course

scope of this course is too small you need more to learn about ML but this course is good to start with.

von Sascha B

21. Juli 2020

I think the course structure is great and provides a good overview of the various machine learning algorithms. In my opinion the coding excercises could dig a little deeper into the subject matter and sometimes a little more detail on the maths behind the algorithms would be beneficial. Overall it is a good introduction.

von Mišo D

15. Jan. 2021

Although a great course some of the materials are outdated. Some codes did not work without importing proper libraries/modules, needed time to figure out. The Watson Studio/IBM cloud looks different now than in the video in the course, so, it takes more time to figure it out.

In summary: Great, but needs an update.

von Folorunsho E

11. Sep. 2019

I had an amazing learning experience in this course. Although, i had challenges understanding some parts of the code, i found that i was able to scale through the capstone project without much stress. To further improve on the experience, it will be nice if some strange codes are properly explained and documented.

von Ruben G

23. Dez. 2020

Great course!

Just a short notice about the final exercise. It would be helpful to guide the students a bit further. I didn't know what to do with so many "blank lines" to fill in. In my opinion, you should whether explain what to do in each line or just leave a "big blank line" where we can write our scripts.